A tunnel disease inspection management method and system based on alliance chain and RFID

Abstract

The present invention provides a tunnel disease inspection management method and system based on alliance chain and RFID, which relates to the field of tunnel disease detection technology, and includes the following steps: S1, receiving the first drone inspection information uploaded by the RFID chip in the current tunnel section; S2, structurally storing the first drone inspection information of the first drone based on the alliance chain; S3, obtaining the initial weight of the first drone in the alliance chain, generating an inspection work sheet according to the initial weight of the first drone and the first drone inspection information, and sending it to the inspection personnel; S4, receiving the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section; S5, receiving the second drone inspection information uploaded by the RFID chip in the current tunnel section; S6, updating the initial weight of the first drone according to the second drone inspection information.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel disease detection, and in particular to a tunnel disease inspection and management method and system based on alliance chain and RFID. Background Art

[0002] Traditional tunnel inspection and defect treatment processes are plagued by inadequate inspections, inefficient defect recording, and cumbersome defect location. Currently, RFID technology is commonly used to address this issue. RFID tags are placed within tunnels, allowing inspectors to identify them with handheld mobile devices and monitor their response to defects. Furthermore, previous RFID systems typically utilize centralized storage and authentication, storing the defect information associated with the tags in back-end data. This large amount of data increases maintenance costs and complexity. Summary of the Invention

[0003] The purpose of the present invention is to provide a tunnel disease inspection and management method and system based on alliance chain and RFID. The method mainly uses RFID chips to divide the interior of the tunnel into multiple tunnel sections. The RFID chips are used to supervise the inspection personnel to reach the disease location and deal with the diseases. In addition, the relevant disease information is stored in a structured form in an alliance chain with a specific access mechanism, realizing functions such as anti-cheating work, highly reliable distributed storage, disease data tracking, and information sharing.

[0004] In order to solve the above technical problems, the present invention adopts the following solutions:

[0005] A tunnel disease inspection and management method based on alliance chain and RFID, wherein a plurality of RFID chips are arranged at intervals along the central axis of the track inside the tunnel, and the RFID chips are used to divide the tunnel into multiple tunnel sections. The tunnel disease inspection and management method includes the following steps:

[0006] S1, receiving the first drone inspection information uploaded by the RFID chip in the current tunnel section;

[0007] The first drone inspection information is obtained by the first drone inspecting the previous tunnel section, including disease information and RFID chip location information in the previous tunnel section;

[0008] S2. Structurally storing the first drone inspection information of the first drone based on the alliance chain;

[0009] S3. Obtain the initial weight of the first drone in the alliance chain, generate an inspection work order based on the initial weight of the first drone and the inspection information of the first drone, and send it to the inspection personnel;

[0010] S4, receiving the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section;

[0011] The inspection personnel processing information is obtained by the processing personnel through processing the previous tunnel section based on the disease information in the previous tunnel section;

[0012] S5, receiving the second drone inspection information uploaded by the RFID chip in the current tunnel section;

[0013] The second drone inspection information is obtained by the second drone inspecting the previous tunnel section along the current spot inspection path, where the current spot inspection path is generated based on the disease information and RFID chip location information in the previous tunnel section corresponding to the information processed by the inspection personnel;

[0014] S6. Update the initial weight of the first UAV according to the inspection information of the second UAV.

[0015] Furthermore, during the inspection of the previous tunnel section by the first drone, the first drone performs disease detection on the interior of the previous tunnel section to obtain the first drone inspection information; then, the first drone flies along the center axis of the track to the current tunnel section, and during the inspection of the current tunnel section, the first drone writes the first drone inspection information corresponding to the previous tunnel section into the RFID chip in the current tunnel section through the RFID chip in the current tunnel section.

[0016] Furthermore, the disease detection refers to the first drone locating the disease inside the tunnel through the multi-sensors it carries, obtaining the disease information through positioning, and packaging the disease information and the RFID chip location information in the tunnel section where the disease information is located to generate the first drone inspection information.

[0017] Furthermore, in S2, the process of structured storage of the first drone inspection information of the first drone based on the alliance chain is as follows:

[0018] The first drone is used as a node of the alliance chain in advance, the received first drone inspection information is preprocessed, the disease information in the preprocessed first drone inspection information is identified, the identification result is encapsulated into a transaction structure, and stored in the corresponding node. Each node corresponds to an initial weight.

[0019] Furthermore, in S3, the process of generating an inspection work order based on the initial weight of the first drone and the inspection information of the first drone and sending it to the inspection personnel is as follows:

[0020] The location information of the disease is obtained from the disease information in the first drone inspection information, and the diseases are prioritized according to the location information of the disease and the initial weight of the first drone corresponding to the disease to obtain the prioritized diseases. An inspection work order is generated according to the prioritized diseases and sent to the inspection personnel.

[0021] Furthermore, in the process of the processing personnel processing the previous tunnel interval according to the disease information in the previous tunnel interval, the processing personnel obtains the disease information in the previous tunnel interval from the inspection work sheet, processes the disease according to the disease information in the previous tunnel interval, records the inspection personnel processing information, and then, the inspection personnel writes the inspection personnel processing information onto the RFID chip in the previous tunnel interval through the RFID chip in the previous tunnel interval.

[0022] Furthermore, in S5, the process of generating the spot check path is as follows:

[0023] The current inspection personnel processing information is obtained, and the inspection personnel's disease location information and the RFID chip location information in the tunnel section where the disease is located are obtained based on the current inspection personnel processing information. The disease location information and the RFID chip location information are used to generate a spot check path, so that the second UAV can perform disease detection on the disease location information.

[0024] Furthermore, step S6 includes the following steps:

[0025] S61: Parse the second drone inspection information to obtain the disease location information of the second drone performing disease detection, and obtain corresponding inspection personnel processing information and the first drone inspection information based on the disease location information;

[0026] S62, extracting disease information from the first drone inspection information and the second drone inspection information respectively, and comparing the disease information for similarity. If similar, proceed to step S63; if not similar, proceed to step S64;

[0027] S63: Determine the actual severity of the disease based on the disease information in the first drone inspection information and the second drone inspection information. If the actual severity does not exceed a threshold, update the initial weight of the first drone.

[0028] S64: extract corresponding processing records from the inspection personnel's processing information, determine the actual severity of the disease based on the processing records, and update the initial weight of the first UAV based on the actual severity.

[0029] A tunnel disease inspection and management system based on alliance chain and RFID, applying the tunnel disease inspection and management method based on alliance chain and RFID, comprising:

[0030] A first drone inspection information collection module receives first drone inspection information uploaded by an RFID chip in the current tunnel section; the first drone inspection information is obtained by the first drone during an inspection of the previous tunnel section, including disease information and RFID chip location information in the previous tunnel section;

[0031] Alliance chain module: Based on the alliance chain, the first drone inspection information of the first drone is structured and stored;

[0032] Inspection work order generation module: obtains the initial weight of the first drone in the alliance chain, generates an inspection work order based on the initial weight of the first drone and the inspection information of the first drone, and sends it to the inspection personnel;

[0033] Inspection personnel processing information collection module: receives the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section; the inspection personnel processing information is obtained by the processing personnel based on the disease information in the previous tunnel section;

[0034] The second drone inspection information collection module receives the second drone inspection information uploaded by the RFID chip in the current tunnel section. The second drone inspection information is obtained by the second drone inspecting the previous tunnel section along the current spot inspection path. The current spot inspection path is generated based on the disease information and RFID chip location information in the previous tunnel section corresponding to the information processed by the inspection personnel.

[0035] Alliance chain update module: updates the initial weight of the first drone based on the inspection information of the second drone.

[0036] Beneficial effects of the present invention:

[0037] The present invention provides a tunnel disease inspection and management method and system based on alliance chain and RFID. The first drone and the second drone are adopted based on the RFID system. During the inspection process, the first drone and the second drone can write the disease information collected in real time into the corresponding RFID tags through the RFID reader / writer, and the inspection personnel can also write the processing information obtained after the disease is treated into the corresponding RFID tags through a handheld mobile terminal. The present invention can judge the correlation between the written information between the first drone, the inspection personnel, the second drone and the RFID, and supervise the inspection personnel to arrive at the scene to treat the disease while supervising the accuracy and effectiveness of the first drone and the inspection personnel in treating the disease, which can greatly improve the efficiency of tunnel disease inspection. In addition, in the present invention, the written information processed by the first drone, the inspection personnel and the second drone is stored in a structured form in an alliance chain with a specific access mechanism, realizing the functions of preventing work cheating, highly reliable distributed storage, disease data tracking, information sharing and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the interior of the tunnel in Example 1 of the present invention;

[0039] Figure 2 This is a flow chart of the tunnel disease inspection and management method in Example 1 of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Unless otherwise specifically stated, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0042] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0043] Additionally, descriptions of well-known structures, functions, and configurations may be omitted for clarity and conciseness. Those skilled in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0044] Technologies, methods and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods and equipment should be considered part of the authorization specification.

[0045] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0046] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments:

[0047] Example 1

[0048] In this embodiment, if Figure 1 As shown, several RFID chips are arranged at intervals inside the tunnel along the central axis of the track, and the inside of the tunnel is divided into multiple tunnel sections by the RFID chips. The distance between the RFID chips can be equal, and the distance can be set according to actual conditions, taking into account the distance required for the inspection personnel to perform disease treatment in the tunnel section and pass through the RFID chip, thereby avoiding increasing the workload of the inspection personnel and facilitating the inspection personnel to write the processing information after the disease treatment through the RFID chip.

[0049] Specifically, a tunnel disease inspection and management method based on alliance chain and RFID is provided, such as Figure 2 As shown, the tunnel disease inspection and management method includes the following steps:

[0050] S1, receiving the first drone inspection information uploaded by the RFID chip in the current tunnel section;

[0051] The first drone inspection information is obtained by the first drone inspecting the previous tunnel section, including disease information and RFID chip location information in the previous tunnel section;

[0052] S2. Structurally storing the first drone inspection information of the first drone based on the alliance chain;

[0053] S3. Obtain the initial weight of the first drone in the alliance chain, generate an inspection work order based on the initial weight of the first drone and the inspection information of the first drone, and send it to the inspection personnel;

[0054] S4, receiving the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section;

[0055] The inspection personnel processing information is obtained by the processing personnel through processing the previous tunnel section based on the disease information in the previous tunnel section;

[0056] S5, receiving the second drone inspection information uploaded by the RFID chip in the current tunnel section;

[0057] The second drone inspection information is obtained by the second drone inspecting the previous tunnel section along the current spot inspection path, where the current spot inspection path is generated based on the disease information and RFID chip location information in the previous tunnel section corresponding to the information processed by the inspection personnel;

[0058] S6. Update the initial weight of the first UAV according to the inspection information of the second UAV.

[0059] In one embodiment, during the process of the first drone inspecting the previous tunnel section, the first drone performs disease detection on the interior of the previous tunnel section to obtain first drone inspection information; then, the first drone flies along the center axis of the track to the current tunnel section, and during the process of inspecting the current tunnel section, the first drone writes the first drone inspection information corresponding to the previous tunnel section onto the RFID chip in the current tunnel section through the RFID chip in the current tunnel section.

[0060] In one embodiment, the disease detection refers to the first drone locating the disease inside the tunnel through the multi-sensors it carries, obtaining the disease information through positioning, and packaging the disease information and the RFID chip location information in the tunnel section where the disease information is located to generate the first drone inspection information.

[0061] Specifically, the present invention uses a first drone to inspect tunnel defects, specifically identifying and locating tunnel defects. These defects include water leaks, cracks, misalignment, spalling, and lining voids. To identify and locate these defects, the first drone is equipped with multiple sensors, including a visible light camera, an infrared thermal imager, a lidar, an ultrasonic sensor, a temperature sensor, a humidity sensor, an optical sensor, and an air pressure sensor. This not only allows for a more comprehensive inspection of the subway tunnel structure, but also provides the drone with a higher degree of freedom, enabling more detailed inspection of minor defects. Using the multiple sensors onboard the first drone, when the first drone identifies and locates a tunnel defect, it records the inspection time, inspection equipment, tunnel name, tunnel temperature and humidity, lighting conditions, defect images, defect type, defect location, and defect severity. The defect information then includes the inspection time, inspection equipment, tunnel name, tunnel temperature and humidity, lighting conditions, defect photos, defect type, defect location, and defect severity.

[0062] Among them, since the present invention has pre-installed several RFID chips inside the tunnel, the tunnel is divided into multiple tunnel sections by the RFID chip, and then one RFID chip corresponds to one tunnel section. When the first UAV identifies and locates the tunnel disease, it obtains the disease information, extracts the tunnel name and disease location from the disease information, and locates the tunnel section where the current disease is located according to the tunnel name and disease location. The RFID chip corresponding to the disease can be obtained according to the tunnel section. Each disease corresponds to an RFID chip. After the inspection personnel handles the disease, they can go to the location of the RFID chip according to the RFID chip location information, so that the back-end can supervise the inspection personnel's handling of the disease, and the inspection personnel can upload the information after the disease is treated to the back-end through the RFID chip.

[0063] In one embodiment, in S2, the process of structurally storing the first drone inspection information of the first drone based on the alliance chain is as follows:

[0064] The first drone is used as a node of the alliance chain in advance, the received first drone inspection information is preprocessed, the disease information in the preprocessed first drone inspection information is identified, the identification result is encapsulated into a transaction structure, and stored in the corresponding node. Each node corresponds to an initial weight.

[0065] Specifically, since existing RFID systems generally use centralized storage and authentication methods to place the disease information associated with the tags in the back-end data, large amounts of data will increase maintenance costs and difficulty. At the same time, the centralized storage method has a single point failure problem, and there is no complete data sharing mechanism and platform between different departments.

[0066] To address the aforementioned technical issues, this invention utilizes a consortium blockchain for structured storage of disease information. Specifically, this approach combines the InterPlanetary File System (IPFS) with the consortium blockchain, improving its scalability and addressing its inability to store images. Smart contracts on the blockchain enable intelligent management of disease information, including disease alarms, inspection task allocation, and disease statistics.

[0067] Consortium blockchains are a specialized type of blockchain that operate within a network of members with varying levels of authority, combining the decentralization of public blockchains with the efficiency of private blockchains. In consortium chains, permissions are allocated and managed by a dedicated consortium management organization, primarily serving a specific, relatively small group of users. Because consortium chains are typically used between specific organizations, users must register through a trusted membership service to obtain permission to read and write blockchain data. Furthermore, the number and status of consortium chain nodes can be effectively monitored and managed. Given these characteristics, consortium chains generally prefer consensus algorithms with low energy consumption and short consensus times to improve overall system efficiency and performance.

[0068] The InterPlanetary File System (IPFS) is a network transmission protocol designed to enable persistent, distributed storage and file sharing. With distributed storage as its core concept, it integrates a variety of advanced technologies and protocols, such as BitTorrent, Git, and SFS. By integrating these technologies, IPFS creates a globally unified addressable space, enabling secure and stable data storage and efficient, real-time data transmission, providing users with a more reliable and efficient data storage and sharing solution.

[0069] Specifically, after receiving the first drone inspection information uploaded by the RFID chip in the current tunnel section, it is necessary to pre-process the first drone inspection information before uploading it to the chain, format the defect information in the first drone inspection information, and encapsulate the formatted defect information into a transaction structure containing information such as the smart contract, timestamp, and random number. The transaction data is then signed using an elliptic curve signature tool to ensure data integrity and non-repudiation. Finally, the signed transaction is serialized for efficient transmission within the network. Once the serialization process is complete, the serialized transaction data can be submitted to the consortium chain, where the consensus nodes verify the submitted transaction to ensure the legitimacy and consistency of the data.

[0070] At this time, in this embodiment, the consensus nodes mainly refer to the first drone, the inspection personnel, and the second drone. The first drone, the inspection personnel, and the second drone are respectively used as a consensus node, and the roles are layered, so that each consensus node can not only store a large amount of data in the node, but also realize the effective transmission of data between consensus nodes and verify their data.

[0071] Specifically, the consortium chain primarily uses an improved BFT algorithm to achieve consensus. The improved BFT algorithm can be combined with a reputation model to adjust node weights, giving high-reputation nodes greater voting power. Under the improved BFT algorithm, the first drone is used to preprocess data and identify defects to initiate a consensus request. The inspection personnel are responsible for reaching consensus on the data submitted by the drones. Verified data is stored through smart contracts, with text stored on the blockchain and images and videos stored in IPFS. Finally, the resulting file hashes are stored on the blockchain. The second drone is used to randomly check the authenticity of the data and adjust the initial weight of the first drone based on the data's authenticity.

[0072] In one embodiment, in S3, the process of generating an inspection work order based on the initial weight of the first drone and the inspection information of the first drone and sending it to the inspection personnel is as follows:

[0073] The location information of the disease is obtained from the disease information in the first drone inspection information, and the diseases are prioritized according to the location information of the disease and the initial weight of the first drone corresponding to the disease to obtain the prioritized diseases. An inspection work order is generated according to the prioritized diseases and sent to the inspection personnel.

[0074] The initial weight is used to mark the accuracy and effectiveness of disease detection by different first drones. Since there are a large number of drones of different specifications on the market, and the accuracy of disease detection of drones will change under the influence of usage time or other conditions, in this embodiment, drones are prioritized according to the accuracy of disease detection, and the diseases are prioritized according to the location information of the diseases and the initial weights of the first drones corresponding to the diseases. Inspection work orders are generated for the prioritized diseases and sent to the inspection personnel, which can improve the effectiveness of the inspection personnel in handling the diseases.

[0075] In one embodiment, when the processing personnel processes the previous tunnel interval based on the disease information in the previous tunnel interval, the processing personnel obtains the disease information in the previous tunnel interval from the inspection work sheet, processes the disease based on the disease information in the previous tunnel interval, records the inspection personnel processing information, and then, the inspection personnel writes the inspection personnel processing information onto the RFID chip in the previous tunnel interval through the RFID chip in the previous tunnel interval.

[0076] Specifically, in the present invention, a second drone is also used to inspect the defects processed by the inspection personnel. The inspection of the second drone is different from the inspection of the first drone. The second drone mainly plays a spot check role on the inspection personnel and the first drone. According to the inspection personnel processing information written by the inspection personnel, the tunnel defects are identified and located according to the spot check path. The second drone identifies and locates the tunnel defects in the same way as the first drone. Multiple sensors are installed on the second drone, including visible light cameras, infrared thermal imagers, lidars, ultrasonic sensors, temperature sensors, humidity sensors, optical sensors and air pressure sensors. Not only can the subway tunnel structure be more comprehensively inspected, but the drone has a higher degree of freedom and can perform more detailed inspections of minor defects.

[0077] In one embodiment, in S5, the process of generating the spot check path is as follows:

[0078] The current inspection personnel processing information is obtained, and the inspection personnel's disease location information and the RFID chip location information in the tunnel section where the disease is located are obtained based on the current inspection personnel processing information. The disease location information and the RFID chip location information are used to generate a spot check path, so that the second UAV can perform disease detection on the disease location information.

[0079] In one embodiment, step S6 includes the following steps:

[0080] S61: Parse the second drone inspection information to obtain the disease location information of the second drone performing disease detection, and obtain corresponding inspection personnel processing information and the first drone inspection information based on the disease location information;

[0081] S62, extracting disease information from the first drone inspection information and the second drone inspection information respectively, and comparing the disease information for similarity. If similar, proceed to step S63; if not similar, proceed to step S64;

[0082] S63: Determine the actual severity of the disease based on the disease information in the first drone inspection information and the second drone inspection information. If the actual severity does not exceed a threshold, update the initial weight of the first drone.

[0083] S64: extract corresponding processing records from the inspection personnel's processing information, determine the actual severity of the disease based on the processing records, and update the initial weight of the first UAV based on the actual severity.

[0084] Specifically, the present invention judges the correlation between the written information between the first drone, the inspection personnel, the second drone and the RFID. First, based on the alliance chain, the first drone inspection information, the inspection personnel processing information, and the second drone inspection information at the same disease location are called. The disease information can be extracted from the first drone inspection information, the inspection personnel processing information, and the second drone inspection information respectively for judgment in sequence. First, the disease image can be extracted from the first drone inspection information and the second drone inspection information respectively, and the similarity of the two disease images taken at different times can be judged based on image recognition. If the disease images of the two are similar, it means that there is no significant change before and after the disease. Among them, other disease information can also be used for auxiliary judgment in the similarity judgment, such as temperature and humidity in the tunnel, lighting conditions, etc.

[0085] In actual scenarios, when the disease images of the two are similar, it may mean that the inspector did not perform disease image processing on the position corresponding to the disease image detected by the first drone. There may be two situations that cause this phenomenon. The first is because the disease image detected by the first drone is inaccurate, so the inspector does not need to perform disease processing on the position corresponding to the disease image; the second is because the inspector himself is negligent or makes mistakes, so the inspector did not perform disease processing on the position corresponding to the disease image detected by the first drone, resulting in the two disease images being similar.

[0086] In this embodiment, different weight adjustments are made to the above two situations based on whether the actual severity of the disease exceeds the threshold value based on the disease information in the first UAV inspection information and the second UAV inspection information. Specifically, when the actual severity identified in the disease image detected by the first UAV does not exceed the threshold value, since the inspection personnel did not deal with the disease, it can be judged that the location corresponding to the disease image detected by the first UAV may not have a disease or the actual severity of the disease is small, and the initial weight of the first UAV is updated.

[0087] Specifically, the actual severity is obtained by performing image recognition on the disease image. For example, wall cracks and crack gaps between the wall cracks are obtained in the current disease image through image recognition. By judging whether the crack gap exceeds a preset wall crack threshold, it is judged whether the actual severity of the disease exceeds the threshold.

[0088] Specifically, in real-world scenarios, the image recognition module onboard a drone may be inaccurate, resulting in target recognition errors in the disease images obtained by the drone inspection. A secondary, accurate disease target recognition backend may reveal that the disease in the identified disease image does not contain any disease. Therefore, if the actual severity level detected in the disease image detected by the first drone does not exceed the threshold, the inspector has not addressed the disease, indicating that the first drone's disease recognition is inaccurate. In this case, the first drone's initial weight is reduced. Conversely, if the actual severity level detected in the disease image detected by the first drone exceeds the threshold, the inspector has not addressed the disease, and supervision can be performed to determine whether the inspector has addressed the disease.

[0089] In real-world scenarios, when the two defect images are dissimilar, it could indicate that the inspector has processed the defect image at the location corresponding to the defect image detected by the first drone. Since different drones may be used for simultaneous flights in different tunnel sections or scenarios, the geographic location or structure of each tunnel section can, to a certain extent, affect the severity of the defects within each tunnel section. For example, if the geological conditions in a tunnel section are poor, cracks may easily form in the tunnel walls, with the crack gaps being much larger than in other sections. Upon detecting this, the first drone's initial weight can be adjusted based on the crack gaps, prioritizing the defects and allowing inspectors to prioritize those with higher severity.

[0090] In this embodiment, different weight adjustments are made to the above situations based on the processing records extracted from the inspection personnel's processing information. The actual severity of the disease can be directly judged based on the processing records. For example, the actual gap of the wall crack recorded by the inspection personnel is compared with the preset wall crack threshold to determine the actual severity of the disease, and the initial weight of the first drone is updated based on the actual severity.

[0091] Specifically, the priority order of the inspection personnel in handling the diseases is adjusted according to the actual severity of the diseases. When the actual severity does not exceed the threshold, it means that the actual severity of the diseases collected by the first drone is low, and the initial weight of the first drone is reduced; when the actual severity exceeds the threshold, it means that the actual severity of the diseases collected by the first drone is high, and the initial weight of the first drone is increased.

[0092] In summary, the present invention adopts a first drone and a second drone based on the RFID system. During the inspection process, the first drone and the second drone can write the disease information collected in real time into the corresponding RFID tag through the RFID reader, and the inspection personnel can also write the processing information obtained after the disease is treated into the corresponding RFID tag through the handheld mobile terminal. The present invention can judge the correlation between the written information between the first drone, the inspection personnel, the second drone and the RFID, and supervise the inspection personnel to arrive at the scene to treat the disease while supervising the accuracy and effectiveness of the first drone and the inspection personnel in treating the disease, which can greatly improve the efficiency of tunnel disease inspection. Moreover, in the present invention, the written information processed by the first drone, the inspection personnel and the second drone is stored in a structured form in a consortium chain with a specific access mechanism, realizing functions such as anti-cheating at work, highly reliable distributed storage, disease data tracking, and information sharing.

[0093] Example 2

[0094] A tunnel disease inspection and management system based on alliance chain and RFID, applying the tunnel disease inspection and management method based on alliance chain and RFID, comprising:

[0095] A first drone inspection information collection module receives first drone inspection information uploaded by an RFID chip in the current tunnel section; the first drone inspection information is obtained by the first drone during an inspection of the previous tunnel section, including disease information and RFID chip location information in the previous tunnel section;

[0096] Alliance chain module: Based on the alliance chain, the first drone inspection information of the first drone is structured and stored;

[0097] Inspection work order generation module: obtains the initial weight of the first drone in the alliance chain, generates an inspection work order based on the initial weight of the first drone and the inspection information of the first drone, and sends it to the inspection personnel;

[0098] Inspection personnel processing information collection module: receives the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section; the inspection personnel processing information is obtained by the processing personnel based on the disease information in the previous tunnel section;

[0099] The second drone inspection information collection module receives the second drone inspection information uploaded by the RFID chip in the current tunnel section. The second drone inspection information is obtained by the second drone inspecting the previous tunnel section along the current spot inspection path. The current spot inspection path is generated based on the disease information and RFID chip location information in the previous tunnel section corresponding to the information processed by the inspection personnel.

[0100] Alliance chain update module: updates the initial weight of the first drone based on the inspection information of the second drone.

[0101] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Based on the technical essence of the present invention and within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement of the above embodiment shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A tunnel disease inspection and management method based on alliance chain and RFID, characterized in that: The tunnel is provided with a plurality of RFID chips spaced along the central axis of the track, and the tunnel is divided into a plurality of tunnel sections by the RFID chips. The tunnel disease inspection and management method includes the following steps: S1, receiving the first drone inspection information uploaded by the RFID chip in the current tunnel section; The first drone inspection information is obtained by the first drone inspecting the previous tunnel section, including disease information and RFID chip location information in the previous tunnel section; S2. Structurally storing the first drone inspection information of the first drone based on the alliance chain; S3. Obtain the initial weight of the first drone in the alliance chain, generate an inspection work order based on the initial weight of the first drone and the inspection information of the first drone, and send it to the inspection personnel. The process is as follows: obtain the location information of the disease from the disease information in the inspection information of the first drone, prioritize the diseases based on the location information of the disease and the initial weight of the first drone corresponding to the disease, obtain the prioritized diseases, generate an inspection work order based on the prioritized diseases, and send it to the inspection personnel; S4, receiving the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section; The inspection personnel processing information is obtained by the processing personnel through processing the previous tunnel section based on the disease information in the previous tunnel section; S5, receiving the second drone inspection information uploaded by the RFID chip in the current tunnel section; The second drone inspection information is obtained by the second drone inspecting the previous tunnel section along the current spot inspection path, where the current spot inspection path is generated based on the disease information and RFID chip location information in the previous tunnel section corresponding to the information processed by the inspection personnel; S6. Update the initial weight of the first UAV based on the inspection information of the second UAV; The step S6 includes the following steps: S61: Parse the second drone inspection information to obtain the disease location information of the second drone performing disease detection, and obtain corresponding inspection personnel processing information and the first drone inspection information based on the disease location information; S62, extracting disease information from the first drone inspection information and the second drone inspection information respectively, and comparing the disease information for similarity. If similar, proceed to step S63; if not similar, proceed to step S64; S63: Determine the actual severity of the disease based on the disease information in the first drone inspection information and the second drone inspection information. If the actual severity does not exceed a threshold, update the initial weight of the first drone. S64: extract corresponding processing records from the inspection personnel's processing information, determine the actual severity of the disease based on the processing records, and update the initial weight of the first UAV based on the actual severity.

2. The tunnel disease inspection and management method based on alliance chain and RFID according to claim 1 is characterized in that: During the inspection of the previous tunnel section by the first drone, the first drone performs disease detection on the interior of the previous tunnel section to obtain first drone inspection information; Then, the first drone flies along the center axis of the track to the current tunnel section. During the inspection of the current tunnel section, the first drone writes the first drone inspection information corresponding to the previous tunnel section into the RFID chip in the current tunnel section through the RFID chip in the current tunnel section.

3. The tunnel disease inspection and management method based on alliance chain and RFID according to claim 2 is characterized in that: The disease detection refers to the first drone locating the disease inside the tunnel through the multi-sensors it carries, obtaining disease information through positioning, and packaging the disease information and the RFID chip location information in the tunnel section where the disease information is located to generate the first drone inspection information.

4. The tunnel disease inspection and management method based on alliance chain and RFID according to claim 1 is characterized in that: In S2, the process of structured storage of the first drone inspection information of the first drone based on the alliance chain is as follows: The first drone is used as a node of the alliance chain in advance, the received first drone inspection information is preprocessed, the disease information in the preprocessed first drone inspection information is identified, the identification result is encapsulated into a transaction structure, and stored in the corresponding node. Each node corresponds to an initial weight.

5. The tunnel disease inspection and management method based on alliance chain and RFID according to claim 1 is characterized in that: In the process of the processing personnel processing the previous tunnel interval according to the disease information in the previous tunnel interval, the processing personnel obtains the disease information in the previous tunnel interval from the inspection work sheet, processes the disease according to the disease information in the previous tunnel interval, records the inspection personnel processing information, and then, the inspection personnel writes the inspection personnel processing information onto the RFID chip in the previous tunnel interval through the RFID chip in the previous tunnel interval.

6. The tunnel disease inspection and management method based on alliance chain and RFID according to claim 5 is characterized in that: In S5, the process of generating the spot check path is as follows: The current inspection personnel processing information is obtained, and the inspection personnel's disease location information and the RFID chip location information in the tunnel section where the disease is located are obtained based on the current inspection personnel processing information. The disease location information and the RFID chip location information are used to generate a spot check path, so that the second UAV can perform disease detection on the disease location information.

7. A tunnel disease inspection and management system based on alliance chain and RFID, characterized by: A tunnel disease inspection and management method based on alliance chain and RFID as described in any one of claims 1 to 6 is applied, comprising: A first drone inspection information collection module receives first drone inspection information uploaded by an RFID chip in the current tunnel section; the first drone inspection information is obtained by the first drone during an inspection of the previous tunnel section, including disease information and RFID chip location information in the previous tunnel section; Alliance chain module: Based on the alliance chain, the first drone inspection information of the first drone is structured and stored; Inspection work order generation module: obtains the initial weight of the first drone in the alliance chain, generates an inspection work order based on the initial weight of the first drone and the inspection information of the first drone, and sends it to the inspection personnel; Inspection personnel processing information collection module: receives the inspection personnel processing information uploaded by the RFID chip in the previous tunnel section; the inspection personnel processing information is obtained by the processing personnel based on the disease information in the previous tunnel section; The second drone inspection information collection module receives the second drone inspection information uploaded by the RFID chip in the current tunnel section. The second drone inspection information is obtained by the second drone inspecting the previous tunnel section along the current spot inspection path. The current spot inspection path is generated based on the disease information and RFID chip location information in the previous tunnel section corresponding to the information processed by the inspection personnel. Alliance chain update module: updates the initial weight of the first drone based on the inspection information of the second drone.

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